基于RBF神经网络整定PID的电液比例系统位置控制研究

针对凿岩机械臂的电液比例系统位置控制精度问题,提出了一种基于径向基函数(RBF)神经网络整定PID的电液比例系统位置控制方法。首先,在AMESim中搭建了阀控非对称液压缸的电液比例系统简化模型,设置了各个模块的参数;然后,利用MATLAB/Simulink搭建了系统闭环控制模型,通过不断更新RBF网络模型并修正PID参数,实现了基于RBF神经网络整定PID的电液比例系统位置控制目的;结合AMESim搭建的电液比例系统模型和Simulink下搭建的控制器进行了联合仿真;最后,基于凿岩台车机械臂实验平台,进行了电液比例系统位置控制实验。仿真结果表明:在受到外部干扰的情况下,RBF神经网络整定PID控制系统能够在0.3 s内控制活塞杆重新运行至目标位置,平均响应时间为1.5 s,位置精度误差不超过5 mm。实验结果表明:与常规PID控制方法相比,RBF神经网络整定PID控制活塞杆位置精度误差降低了75%,位置精度误差在工程实际要求的10 mm范围以内,因此,RBF神经网络整定PID算法可以有效提高电液比例系统的位置控制精度,满足凿岩机械臂实际工作中对电液比例系统位置精度的控制要求。

基于改进MPC与RBF-PID的智能车轨迹跟踪控制

为提升智能汽车轨迹跟踪控制的稳定性和精度,提出一种基于时域参数自适应MPC与RBF-PID的轨迹跟踪控制方法.首先搭建车辆横纵向动力学模型;其次分析控制时域与预测时域对跟踪精度的影响,设计时域参数自适应MPC横向控制器并进行仿真验证;然后基于径向基函数(RBF)神经网络整定PID控制参数,设计分层式纵向控制器,并设计折线速度曲线与PID算法进行对比;最后以车速为耦合点构建智能汽车横纵向综合控制系统,并在蛇形工况下对横纵向综合控制系统进行仿真实验.结果表明,所设计的横向控制器能保证低速时的跟踪精度和高速时的车辆稳定性,纵向控制器可有效提高速度跟踪精度,横纵向综合控制系统能实现车辆在变车速工况下对轨迹的精准跟踪,同时保证良好的行驶稳定性与舒适性.

基于RBF-PID的掘进机截齿动载荷平稳性控制方法研究

依据实际情况,运用有限元仿真对掘进机截割头水平截割、垂直截割与纵向钻进工况进行仿真。通过对仿真结果进行统计,得到了各截齿在截割过程中截割阻力的大小,并运用仿真结果对截割阻力计算公式进行了修正。通过建立多目标优化模型,运用NSGA-Ⅱ算法进行求解,得到了不同煤岩硬度下的最优截割参数。以上述研究为基础,提出了基于RBF-PID的掘进机截齿动载荷平稳性控制方法。采用MATLAB与AMESim联合仿真,搭建了截割头转速与摆速控制模型,通过仿真并分析仿真结果,验证了所提出方法的可靠性。

基于CDKF-RBFPID的激光器恒流源控制器

激光器的稳定工作需要一个输出电流精度高和稳定的恒流源控制系统。针对激光器恒流源系统在噪声环境下输出精度差且使用PID算法参数难整定的问题,提出一种基于中心差分卡尔曼滤波(Center Differential Kalman Filter,CDKF)与改进的径向基函数(Radial Basis Function,RBF)神经网络自适应PID控制相结合的算法CDKF-RBFPID。通过CDKF更新恒流源系统的状态、协方差,从而滤除系统中的状态噪声和测量噪声。利用强化学习Actor-Critic框架调整RBF-PID参数,实现自适应参数调整。对恒流源系统输出电流和激光器输出功率进行对比实验,结果表明:CDKF RBFPID算法能够有效降低噪声对系统的影响,恒流源输出电流精度以及激光器输出功率稳定性进一步提升,其中响应时间缩短了58.3%,稳态误差降低了71.4%,输出电流控制精度达到1%。

基于改进RBF/模糊PID的锻压机双缸闭环同步信号控制

为了提高连杆驱动锻压机双缸同步控制精度,设计了一种推进过程改进RBF/模糊PID控制方案,采用闭环同步控制。构建了改进RBF/模糊PID控制器,并通过Simulink完成仿真测试,验证了推进过程的同步控制性能。研究结果表明:无论是高速还是低速,改进RBF/模糊PID相对模糊PID控制方式表现出来更低的双缸同步误差,可以获得更高的系统控制精度。

基于RBF神经网络PID算法的锅炉水温控制与研究

针对过程控制实验中水温控制的PID算法参数难以整定的问题,提出了一种基于径向基函数(Radial Basis Function,RBF)神经网络整定PID的算法,并以仿真与实验相结合的方式应用于水温控制实验中。通过使用阶跃响应曲线法辨识锅炉水温数学模型;设计用RBF神经网络整定PID参数的方法,针对对象模型进行仿真;最后将整定的PID参数应用于水温控制实验验证,并与常规PID方法进行实验比较。结果表明,RBF神经网络整定PID控制器的控制方法具有更好的鲁棒性和自适应性,能取得良好的控制效果。

基于RBF神经网络的汽车内饰皮革智能切割系统设计

随着汽车用皮革的迅速发展,开发一套满足汽车内饰皮革生产需求的智能切割系统具有重要意义。本文简述了汽车内饰皮革切割系统的发展,构建了基于径向基函数(Radial Basis Function,RBF)神经网络的汽车内饰皮革智能切割系统,介绍了系统主要硬件配置选型和软件的设计,提出了基于RBF神经网络PID(Proportional Integral Derivative,比例-积分-微分)控制算法;通过搭建试验平台,测试汽车内饰皮革智能切割系统的可行性、切割精度与效率。结果表明,该系统能够较好地满足汽车内饰皮革切割方面的需求。

基于模糊PID汽车变速箱自动控制方法

为改善汽车变速箱的目标速度跟踪水平,提升汽车的动力性,基于模糊PID汽车变速箱自动控制方法,确定汽车行驶的目标车速,计算目标车速与实际车速之间的偏差,以及速度偏差的变化率,将上述结果作为PID控制器的输入,利用参数整定后的PID控制器,确定汽车变速箱的控制变量,控制汽车传动机构输出理想车速。实验结果表明,汽车以阶跃信号、斜坡信号和正弦信号状态行驶时,该方法均可以精准跟踪汽车变速箱的速比,控制效果与动态跟踪性能优异,为保持汽车的最佳动力性能提供了良好的控制策略。

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

Design and Implementation of Digital Fractional Order PID Controller Using Optimal Pole-Zero Approximation Method for Magnetic Levitation System

The aim of this paper is to employ fractional order proportional integral derivative(FO-PID)controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system(MLS),which is inherently nonlinear and unstable system.The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller.An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored.The controller parameters are tuned using dynamic particle swarm optimization(d PSO)technique.Effectiveness of the proposed control scheme is verified by simulation and experimental results.The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers.It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.

基于RBF神经网络的油气悬架平顺性研究

目的 提高整车的行驶性能,使车辆可以自主调节,以适应各种不同的路面,从而达到良好的缓冲效果。方法 以双气室油气弹簧为研究对象,对其组成和工作原理进行详细分析,推导出油气弹簧的阻尼力和刚度等非线性特性与车身位移的关系。根据RBF神经网络控制原理,设计RBF-PID控制器,依靠神经网络自学习性对PID参数动态整定,使整个车身振动衰减,快速达到稳定状态,并基于Matlab/Simulink平台建立仿真模型,在B级和C级不平路面输入的情况下,重点对轮胎动载荷、车身质心加速度以及油气悬挂动挠度等3项性能进行仿真和分析。结果 同普通PID控制相比,RBF-PID控制下,车身3项性能的RMS分别降低26%、54%、0.1%。结论 RBF-PID控制能够克服环境影响,实现油气弹簧特性的可靠控制,提高了车辆行驶的平顺性以及稳定性。

四旋翼无人机预设性能自适应PID控制

针对四旋翼飞行器在轨迹跟踪过程中存在建模误差和外界干扰问题,设计了一种双闭环控制系统。内环姿态环采用自适应PID算法,用滑模算法作为自适应机制,结合梯度下降法克服传统PID需要手动调节参数的问题,并用RBF神经网络消除滑模控制过程中产生的抖振现象;外环位置环采用预设性能自适应PID算法,即在自适应PID算法的基础上加上预设性能控制,将误差用预设性能函数进行转换,使系统误差能够始终稳定在预设值,实现位置的快速跟踪;最后用Lyapunov函数证明系统的稳定性。从跟踪的快速性、稳定性和稳态性能方面,由仿真结果对比证明本文所设计的控制算法有很大的优越性,并能对不同形式的外部扰动表现出强抗干扰性。

基于模糊控制的闪蒸罐PID控制器设计与仿真

PID控制器依照经验进行整定的方式需要花费大量时间和精力,而工业被控对象大都存在随时间变化、非线性等特性,PID控制的效果并不理想。以闪蒸罐压力控制为研究对象设计FUZZY-PID控制器。仿真结果表明:在PID调节后有超调情况下,FUZZY-PID控制器调节时间缩短约341.80 s,超调量减小约4.62%;在PID调节后无超调情况下,FUZZY-PID控制器调节时间缩短约395.60 s。抗干扰实验结果表明:在加入白噪声后,FUZZY-PID控制器与PID控制器控制效果差距不甚明显,都比较稳定,在同一时刻加入脉冲扰动后,FUZZY-PID控制器所需调节时间较PID控制器缩短约70.70 s,调节时间更短,更加稳定。说明相较于传统PID控制器,FUZZY-PID控制器调节时间更短、超调量更小、更加稳定。

Adaptive Quasi-PID Control Method for Switching Power Amplifiers

Quasi-PID control method that is able to effectively inhibit the inherent tracking error of PI control method is proposed on the basis of a rounded theoretical analysis of a model of switching power amplifiers (SPAs). To avoid the harmful impacts of the circuit parameter variations and the random disturbances on quasi-PID control method, a single neuron is introduced to endow it with self-adaptability. Quasi-PID control method and the single neuron combine with each other perfectly, and their formation is named as single-neuron adaptive quasi-PID control method. Simulation and experimental results show that single-neuron adaptive quasi-PID control method can accurately track both the predictable and the unpredictable waveforms. Quantitative analysis demonstrates that the accuracy of single-neuron adaptive quasi-PID control method is comparable to that of linear power amplifiers (LPAs) and so can fulfill the requirements of some high-accuracy applications, such as protective relay test. Such accuracy is very difficult to be achieved by many modern control methods for converter controls. Compared with other modern control methods, the programming realization of single-neuron adaptive quasi-PID control method is more suitable for real-time applications and realization on low-end microprocessors for its simple structure and lower computational complexity.

典型二阶系统的单纯形法PID参数优化设计

针对典型二阶系统,提出了一种基于单纯形法的PID参数优化设计方法。通过对目标函数进行建立和分析,将PID控制器引入系统中,再通过使用单纯形法对PID参数进行优化,得到最优的参数组合,以实现对系统的稳定性和性能的优化。实验结果表明,提出的单纯形法得到的最优参数能有效改善二阶系统的控制性能,并具有一定的实用价值。

Development of an Enhanced Self-Tuning RBF-PID Controller for Achieving Higher Energy-Efficient Process Control

Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural network PID (RBF-PID) is developed and used. Even though being popular, during the control process both of PID and RBF-PID control strategy are inadequate in achieving simultaneous high energy-efficiency and good control accuracy. To address this problem, in this paper we develop and report an enhanced self-tuning radial-basis-function neural network PID (e-RBF-PID) controller. To identify the superiority of e-RBF-PID, following works are conducted and reported in this paper. Firstly, four controllers, i.e., on-off, PID, RBF-PID and e-RBF-PID are designed. Secondly, in order to test the performance of the e-RBF-PID controller, an experimental water heating system is constructed for being controlled. Finally, the energy consumption for the four controllers under the three control scenarios is investigated through experiments. The experimental results indicate that in the three scenarios, the developed e-RBF-PID controller outperforms on-off controller as having higher accuracy. Compared to the PID controller, the e-RBF-PID controller has higher speed in control, and the experimental results show that settling time savings is between 12.6% - 49.0%. Most importantly, less control energy consumption is obtained if using the e-RBF-PID controller. It is found that up to 28.5% energy consumption can be saved. Therefore, it is concluded that the proposed e-RBF-PID is capable of enhancing energy efficiency during control process.

基于RBF神经网络优化光伏储能并网自适应控制的方法研究

常规的光伏储能并网自适应控制,主要采用电压动态调节实现,忽略了并网电流波形偏差对控制结果造成的影响,导致控制结果超调量较大。因此,提出基于RBF神经网络优化光伏储能并网自适应控制的方法。根据光伏储能并网的等效电路分析输出特性,建立基于RBF神经网络的等效电路连接架构,辨识储能作用方式,基于辨识分类结果计算并网电流的谐波补偿值,分析补偿后的内环电流,引入能量需求参数对并网输出功率控制策略进行自适应优化。实验结果表明,所提方法应用后得出的控制结果,表现出的超调量较低,仅为1.8%,控制效果较优,满足了光伏储能并网的现实应用需求。

基于RBF神经网络的双馈风电机组最大功率追踪控制方法

双馈风电机组通过转子回路进行功率调节控制,当系统在最大功率点附近工作时,回路电流会出现高频振荡的问题。这种振荡会影响系统稳定性,最大功率追踪控制是解决该问题的核心手段。提出基于径向基函数(Radial Basis Function,RBF)神经网络的双馈风电机组最大功率追踪控制方法。分析双馈风电机组基本结构与运行原理,获取风电机组输出功率,并将其输入RBF神经网络输入层中,通过网络隐含层对其展开优化训练,再通过输出层输出最大功率。采用二阶滑模控制策略与比例-积分-微分(Proportion Integration Differentiation,PID)控制器相结合的方法对发电机转矩展开追踪控制,实现对双馈风电机组最大功率的追踪控制,使风电机组保持最大功率运行。实验结果表明,所提方法追踪效果好、追踪精度高、控制精度高。

The study of film tension control system based on RBF neural network and PID

In the BOPP （Biaxially Oriented Polypropylene） production line, the tension size and smooth film received change volume has a decisive effect on the rolling quality, casting machine is a complicated electromechanical control system, tension control of casting machine are the main factors that influence the production quality. Analyzed the reason and the tension control mathematical model generation casting machine tension in the BOPP production line, for the constant tension control of casting machine, put forward a kind of improved PID control method based on RBF neural network. By the method of Jacobian information identification of RBF neural network, combined with the incremental PID algorithm to realize the self-tuning tension control parameters, control simulation and implementation of the model using Matlab software programming. The simulation results show that, the improved algorithm has better control effect than the general PID.

基于模糊PID算法的阀门电动执行器控制方法

由于阀门电动执行器具有非线性、时变性等特点,导致传统控制方法很难达到理想的控制效果,提出基于模糊PID算法的阀门电动执行器控制方法。根据阀门电动执行器的结构特点,建立驱动电机环节的数学模型作为被控对象,在传统PID控制器中加入模糊理论形成模糊PID控制结构,输入相应参数得到控制信号,并将其输出到被控对象中,实现对阀门电动执行器的控制。实验结果表明,在设计方法控制阀门电动执行器的过程中施加突变信号扰动后,仅需0.58 s恢复稳态,证实了该方法抗干扰能力强、控制效果优。